1. Field of the Invention
The present invention relates to a face recognition apparatus, a face recognition method, a Gabor filter application apparatus, and a computer program, for recognizing a face in an image such as a photographic picture, and more particularly, to a face recognition apparatus, a face recognition method, a Gabor filter application apparatus, and a computer program, for recognizing a face by extracting a feature value of a face image by using a Gabor filter representing spatial characteristics based on a Gaussian function representing a window and a sine or cosine function representing a frequency response.
The present invention also relates to a face recognition apparatus, a face recognition method, a Gabor filter application apparatus, and a computer program, for recognizing a face image with high accuracy without fixing types or the number of Gabor filter coefficients, and more particularly, to a face recognition apparatus, a face recognition method, a Gabor filter application apparatus, and a computer program, for performing high-performance recognition of a face image by using a Gabor filter without needing an increase in hardware complexity or an increase in processing complexity.
2. Description of the Related Art
The face recognition technology has a wide variety of applications such as a person authentication system capable of authenticating a person without the person needing to perform a particular operation, a system of detecting sex of a person, and other many man-machine interfaces. The current trend in the face recognition technology is to use a front face image, although use of a side face image was tried in the past.
A face recognition system includes a face extraction process for extracting a face pattern from an image taken by a CCD camera or the like, and a face recognition process for recognizing a face based on the extracted face pattern. The face extraction process for extracting a face pattern (or extracting a feature value of a face image) and the face recognition process can be accomplished, for example, by performing Gabor filtering using a plurality of filters having direction selectivity and having different frequency characteristics (see, for example, Japanese Unexamined Patent Application Publication No. 2006-4041).
It is known that human photoreceptor cell include cells having selectivity in a particular direction. The direction selectivity is achieved by a combination of a cell which fires vertically and a cell which responds horizontally. Similarly, a Gabor filter is spatial filter including a plurality of filters having direction selectivity.
The Gabor filter represents a spatial characteristic using a Gabor function including a Gaussian function representing a window and a sine or cosine function indicating a frequency response. The filter window size is fixed at, for example, 24×24 pixels. If one Gabor filter is prepared for each of 5 different frequency f and for each of 8 different directions, a total of 40 Gabor filters are prepared.
The operation by a Gabor filter includes determining the convolution of a pixel value to which the Gabor filter is applied and coefficients of the Gabor filter. The coefficients of the Gabor filter can be divided into real components given by a cosine function representing a frequency response and imaginary components given by a cosine function representing a frequency response. The convolution is calculated separately for the real and imaginary components, and resultant convolution components are added to obtain one scalar value indicating the final result of the Gabor filtering. If up to 40 Gabor filters with various frequencies f and various angles 0 are used, a total of up to 40 scalar values are obtained. A set of the resultant 40 scalar values is referred to as a Gabor jet. In the face recognition process, the Gabor jet is determined as a local feature value at each of feature value extraction positions located at equal intervals in horizontal and vertical directions on given face image data. Note that the Gabor jet does not change even if the feature value extraction position varies within a certain range or if the image is deformed to a certain degree.
For each of registered face images, Gabor jets are calculated in advance for respective feature value extraction positions. When a face image to be examined is given (hereinafter, such a face image will be referred to simply as an input face image), the similarity of a Gabor jet of the input face image with respect to the Gabor jet of the registered face image is determined for each feature value extraction position, and a similarity vector each element of which indicates similarity at one feature value extraction position. Thereafter, a support vector machine (SVM) performs classification based on the similarity vector to determine whether the input face image is identical to a registered face image. More specifically, the support vector machine calculates the distance of the similarity vector of interest from a boundary plane (in which the distance is equal to 0) and determines whether the similarity vector belongs to an intra-personal class or an extra-personal class. If the similarity vector is determined as not belonging to an intra-personal class for any registered face image, it is determined that the input face image is of a person whose face is not yet registered (see, for example, B. Scholkopf et al., “Advance in Kernel Support Vector Learning” (The MIT Press, 1999)). If the support vector machine learns many face images (i.e., if many face images are registered), it is possible to determine whether an input face image is identical to one of registered (learned) face images, that is, whether the input face image belongs to an intra-personal class or an extra-personal class (see, for example, Domestic Republication WO03/019475 or Japanese Unexamined Patent Application Publication No. 2006-4003). The current evaluation for the support vector machine in the pattern recognition technology is that the support vector machine has highest learning ability.